Farahnaz Soleimani 
- (2022): State-of-the-Art Review on Probabilistic Seismic Demand Models of Bridges: Machine-Learning Application. Dans: Infrastructures, v. 7, n. 5 (mai 2022).
- (2022): Bridge seismic hazard resilience assessment with ensemble machine learning. Dans: Structures, v. 38 (avril 2022).
- (2022): Probabilistic seismic analysis of bridges through machine learning approaches. Dans: Structures, v. 38 (avril 2022).
- (2022): Artificial neural network application in predicting probabilistic seismic demands of bridge components. Dans: Earthquake Engineering and Structural Dynamics, v. 51, n. 3 (mars 2022).
- (2021): Machine-learning-based predictive models for estimating seismically-induced slope displacements. Dans: Soil Dynamics and Earthquake Engineering, v. 148 (septembre 2021).
- (2021): Pattern Recognition of the Seismic Demands for Tall Pier Bridge Systems. Dans: Journal of Earthquake Engineering, v. 26, n. 12 (juillet 2021).
- (2021): Analytical seismic performance and sensitivity evaluation of bridges based on random decision forest framework. Dans: Structures, v. 32 (août 2021).
- (2020): Propagation and quantification of uncertainty in the vulnerability estimation of tall concrete bridges. Dans: Engineering Structures, v. 202 (janvier 2020).
- (2016): Cyclic Testing and Assessment of Columns Containing Recycled Concrete Debris. Dans: ACI Structural Journal, v. 113, n. 5 (septembre 2016).
- (2017): Identification of the significant uncertain parameters in the seismic response of irregular bridges. Dans: Engineering Structures, v. 141 (juin 2017).
- (2017): Bridge classes for regional seismic risk assessment: Improving HAZUS models. Dans: Engineering Structures, v. 148 (octobre 2017).
- (2017): A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes. Dans: Engineering Structures, v. 153 (décembre 2017).
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